A position estimating technique has been proposed in which a mobile terminal makes wireless communications with a plurality of base stations, and estimates a location of the mobile terminal by utilizing attenuation of received signal strengths depending on a distance to the mobile terminal from each of the plurality of base stations. For example, the base station may be an AP (Access Point) used in WiFi (Wireless Fidelity, registered trademark).
According to such a position estimating technique, the mobile terminal collects, in advance, IDs (Identifiers) of the plurality of base stations and RSSIs (Received Signal Strength Indicators) received at each location. From the IDs of the plurality of base stations and values of the RSSIs received at each location, an RSSI feature vector that is uniquely determined for each location is created, and a location model is created for each location using the RSSI feature vector. The location model may make a reference to a database indicating the location where the signals are received from the base stations, the base stations from which the signals are received by the mobile terminal at the location, and the RSSIs of the signals received by the mobile terminal at the location. When estimating the location, the RSSIs of the signals received by the mobile terminal from the base stations are collated with the location model, in order to estimate the location of the mobile terminal. Generally, the location model may be created by methods such as the k-NN (k-Nearest Neighbor algorithm) method, probability method based on probability distribution, non-parametric method, pattern matching method, or the like.
In order to improve the position estimating accuracy, it is desirable to create the location model by learning from a large number of learning samples. However, in order to collect the large number of learning samples, an operator of a location detection system must collect RSSI samples by moving to all target locations, and the load on the operator to collect the RSSI samples increases as the number of target locations increases. Hence, in order to reduce the load on the operator, it may be conceivable to create an initial location model based on the RSSI samples collected at a relatively small number of locations, and to periodically update the initial location model, for example.
However, because the conceivable method collects the RSSI samples from the relatively small number of locations and creates the initial location model based on the collected RSSI samples, it is difficult to improve the position estimating accuracy based on the initial location model. In addition, in order to obtain reliable RSSI samples for use in updating the initial location model, the operator must move to the target locations and collect the RSSI samples. For this reason, the load on the operator increases as the number of target locations increases, similarly as in the case in which the location model is created. Further, when the RSSI samples collected by people other than the operator are used to update the initial location model, the initial location model may be updated by erroneous data since the reliability of the RSSI samples collected by the people other than the operator is unknown. If the initial location model is updated based on erroneous data, the position estimating accuracy deteriorates.
Accordingly, it is conventionally difficult to update the location model without increasing the load on the operator of the location detection system, or without deteriorating the position estimating accuracy.
Examples of the related art include Japanese Laid-Open Patent Publications No. 2011-58928, No. 2009-272742, and No. 2009-55138.